Multimodal Racing Agents for Real-Time Sports Data
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Solution Overview
Problem
Existing solutions are unable to generate accurate information relating to sports events, particularly in real-time, using generative AI techniques, and fail to effectively utilize user inputs such as text, audio, and video to perform agentic actions in sports applications.
Innovation Solution
A multimodal sports learning language model (LLM) processes user inputs like text, audio, and video to determine contextual and intentional information, mapping metadata to generate sports tracking data, which is used by agents to perform actions such as generating highlights, narratives, or simulating player movements, utilizing a racing specific orchestrator and agents trained in sport-specific languages to execute instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing generative AI techniques are used to process user inputs, then the system can generate basic content, but the accuracy and performance of generating sports-related data and actions is insufficient
Solution Approach 1:
The system segments the complex task of sports data generation into multiple specialized agents, each responsible for specific functions such as natural language processing, computer vision analysis, data retrieval, and content generation. This segmentation allows each agent to specialize in particular aspects, improving overall accuracy while maintaining versatility through coordinated multi-agent operation.
Solution Approach 2:
The system implements a universal multi-modal framework that can process various input types (text, audio, video) and perform diverse agentic actions (generating highlights, narratives, simulations). The orchestrator coordinates multiple agents to handle different modalities and tasks within a single unified system, achieving both high accuracy and broad adaptability.
2Loss of information
If a simple processing system is used, then the system is easy to operate, but it cannot effectively determine contextual and intentional information from user inputs
Solution Approach 1:
The system introduces an orchestrator as an intermediary component that coordinates between the superior orchestrator, sport-specific orchestrators, and multiple agents. This intermediary structure enables complex information processing and contextual understanding while maintaining a manageable system architecture through clear separation of concerns and standardized communication protocols.
Solution Approach 2:
The system adds a hierarchical dimension to the architecture, with superior orchestrators selecting sport-specific orchestrators, which in turn coordinate multiple agents. This multi-level hierarchical structure enables sophisticated information processing without overwhelming complexity at any single level, allowing effective extraction of contextual and intentional information.
3Speed
If real-time processing is implemented, then the system can provide timely sports data, but the computational complexity and resource requirements increase
Solution Approach 1:
The system performs preliminary processing by having agents retrieve and prepare data in advance, and by using pre-trained models for various modalities. The orchestrator coordinates these pre-prepared elements to quickly assemble final outputs in real-time, reducing computational complexity during critical processing moments while maintaining high speed performance.
Data Source
AI summary
Disclosed techniques relate to using one or more of racing event statistics, textual insights, predictions (e.g., team and player at the event level, and team at the season level), graphics, video overlays, and player and ball tracking data. Tracking data may be generated using an in-venue feed or a broadcast feed. The tracking data may be supplemented with event data which may be provided by an operator or an automated system based on the events related to a given sport within a venue or via a broadcast feed. The tracking data and/or event data may be used to generate insights such as event statistics, textual insights, predictions, graphics, video overlays, and or the like. Accordingly, the tracking data and insights generated in accordance with the subject matter disclosed herein may be specific to a given sporting event and/or the sport associated with the sporting event.